GeomNet: A Neural Network Based on Riemannian Geometries of SPD Matrix Space and Cholesky Space for 3D Skeleton-Based Interaction Recognition

Xuan Son Nguyen (1, 2)

1 ENSEA - Ecole Nationale Supérieure de l'Electronique et de ses Applications
2 ETIS - UMR 8051 - Equipes Traitement de l'Information et Systèmes

In this paper, we propose a novel method for representation and classification of two-person interactions from 3D skeleton sequences. The key idea of our approach is to use Gaussian distributions to capture statistics on R n and those on the space of symmetric positive definite (SPD) matrices. The main challenge is how to parametrize those distributions. Towards this end, we develop methods for embedding Gaussian distributions in matrix groups based on the theory of Lie groups and Riemannian symmetric spaces. Our method relies on the Riemannian geometry of the underlying manifolds and has the advantage of encoding highorder statistics from 3D joint positions. We show that the proposed method achieves competitive results in two-person interaction recognition on three benchmarks for 3D human activity understanding.

Domaines: Intelligence artificielle [cs.AI]

Xuan Son Nguyen. GeomNet: A Neural Network Based on Riemannian Geometries of SPD Matrix Space and Cholesky Space for 3D Skeleton-Based Interaction Recognition. 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Oct 2021, Montreal, Canada. ⟨10.1109/ICCV48922.2021.01313⟩. ⟨hal-03720244⟩